The present invention, in some embodiments thereof, relates to operational decision management and, more specifically, but not exclusively, to visual display of data, visual display of rule results, and modification of results for rule adjustment.
Operational Decision Management is a growing discipline of importance for organizations who seek to improve and automate information-based decisions. Technologies such as Business Rule Management System and Event Processing Systems enable organization to design, manage and execute business logic rules and policies in operational systems. Such tools also provide an interface for the business users to be able to customize the rules and be as self-sufficient as possible in order to respond to changing business needs. The goal is to minimize dependencies on technical support personnel to accomplish these tasks.
For example, the rule that needs to be configured is a series of steps, each step may be an expression such as “If [[attribute_A=X] and [attribute_B>Y]] Then [action]” and the business user selects the attributes and values to form such an expression. In this example, attribute_A and attribute_B may denote attributes of a data element, and the like, such as an incoming event data element. For example, the event may be saved as a record of a database where attribute_A and attribute_B may denote field variables of the data record in the a database, In this example, X and Y denote threshold values to compare to the respective values of field variables of a data record of a database. In this example, action denotes a possible result value assignment, an internal variable assignment, an expression, or another rule step. The steps of a rule may be implemented as computing instructions to be performed on the data, and completion of the processing steps determines the rule result.
The rule based classification determines when a specific data record belongs to a class of records or not, also referred to by the term binary classification. The classification rule is a set of computations performed on values of a data record, such as a record of a database, which determines when the predicted classification of the data records is exhibits the rule or not. The rule result determination is the predicted result of applying the rule to the data record and in binary classification is a true or false predicted result value. The predicted result is compared to known true outcomes from test cases to measure the performance of the rule. For example, data records the rule predicted as true and are actually true are called true positives. For example, data records the rule predicted as false and are actually false are called true negatives. False positives, or type I errors, are data records with true rule predictions but actual false outcomes. False negatives, or type II errors, are data records with false rule predictions but actual true outcomes. Sensitivity is the sum of true positives divided by the total actual positives (sum of true positives and false negatives). Specificity is the sum of true negatives divided by the total actual negatives (sum of true negatives and false positives). Precision is the sum of true positives divided by the total predicted positives (sum of true positives and false positives). Accuracy is the sum of true positives and true negatives divided by the total number of data records. The terms classification rule measures described herein may be described in a contingency table.
Existing methods allow using clustering algorithm results for visualization. However the user needs to change the clustering algorithm parameters manually by manually adjusting each individual parameter value separately.
In U.S. provisional patent application Ser. No. 13/955,005, incorporated herein in its entirety by reference, it has been described how a rule is defined from visual selection.